Overview

Dataset statistics

Number of variables73
Number of observations20
Missing cells784
Missing cells (%)53.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory1.1 KiB

Variable types

Categorical15
DateTime3
Text5
Boolean1
Numeric16
Unsupported33

Alerts

season has constant value "2002"Constant
game_type has constant value "regular"Constant
neutral has constant value "False"Constant
processed_time has constant value "2025-12-07 22:33:30"Constant
attendance is highly overall correlated with away_lon and 7 other fieldsHigh correlation
away_lat is highly overall correlated with q2_away and 2 other fieldsHigh correlation
away_lon is highly overall correlated with attendance and 3 other fieldsHigh correlation
conf_home is highly overall correlated with attendance and 5 other fieldsHigh correlation
game_lat is highly overall correlated with home_lat and 4 other fieldsHigh correlation
game_lon is highly overall correlated with home_lon and 4 other fieldsHigh correlation
home_lat is highly overall correlated with game_lat and 4 other fieldsHigh correlation
home_lon is highly overall correlated with game_lon and 4 other fieldsHigh correlation
ot_away is highly overall correlated with conf_home and 5 other fieldsHigh correlation
ot_home is highly overall correlated with conf_home and 5 other fieldsHigh correlation
q1_away is highly overall correlated with away_lon and 9 other fieldsHigh correlation
q1_home is highly overall correlated with rank_away and 2 other fieldsHigh correlation
q2_away is highly overall correlated with away_lat and 5 other fieldsHigh correlation
q2_home is highly overall correlated with q3_home and 3 other fieldsHigh correlation
q3_away is highly overall correlated with q1_away and 4 other fieldsHigh correlation
q3_home is highly overall correlated with attendance and 3 other fieldsHigh correlation
q4_away is highly overall correlated with q3_away and 2 other fieldsHigh correlation
q4_home is highly overall correlated with ot_away and 4 other fieldsHigh correlation
rank_away is highly overall correlated with attendance and 26 other fieldsHigh correlation
rank_home is highly overall correlated with attendance and 26 other fieldsHigh correlation
score_away is highly overall correlated with rank_away and 1 other fieldsHigh correlation
score_home is highly overall correlated with q1_home and 2 other fieldsHigh correlation
weather_code is highly overall correlated with rank_away and 3 other fieldsHigh correlation
weather_humidity_2m is highly overall correlated with rank_away and 2 other fieldsHigh correlation
weather_precipitation is highly overall correlated with rank_away and 4 other fieldsHigh correlation
weather_temperature_2m is highly overall correlated with attendance and 8 other fieldsHigh correlation
weather_wind_direction_10m is highly overall correlated with attendance and 5 other fieldsHigh correlation
weather_wind_speed_10m is highly overall correlated with game_lon and 3 other fieldsHigh correlation
week is highly overall correlated with attendance and 4 other fieldsHigh correlation
ot_away is highly imbalanced (58.6%)Imbalance
ot_home is highly imbalanced (58.6%)Imbalance
weather_precipitation is highly imbalanced (64.1%)Imbalance
rank_away has 18 (90.0%) missing valuesMissing
rank_home has 16 (80.0%) missing valuesMissing
conf_away has 9 (45.0%) missing valuesMissing
q1_away has 8 (40.0%) missing valuesMissing
q2_away has 8 (40.0%) missing valuesMissing
q3_away has 8 (40.0%) missing valuesMissing
q4_away has 8 (40.0%) missing valuesMissing
ot_away has 8 (40.0%) missing valuesMissing
q1_home has 8 (40.0%) missing valuesMissing
q2_home has 8 (40.0%) missing valuesMissing
q3_home has 8 (40.0%) missing valuesMissing
q4_home has 8 (40.0%) missing valuesMissing
ot_home has 8 (40.0%) missing valuesMissing
first_downs_away has 20 (100.0%) missing valuesMissing
first_downs_home has 20 (100.0%) missing valuesMissing
third_down_comp_away has 20 (100.0%) missing valuesMissing
third_down_att_away has 20 (100.0%) missing valuesMissing
third_down_comp_home has 20 (100.0%) missing valuesMissing
third_down_att_home has 20 (100.0%) missing valuesMissing
fourth_down_comp_away has 20 (100.0%) missing valuesMissing
fourth_down_att_away has 20 (100.0%) missing valuesMissing
fourth_down_comp_home has 20 (100.0%) missing valuesMissing
fourth_down_att_home has 20 (100.0%) missing valuesMissing
pass_comp_away has 20 (100.0%) missing valuesMissing
pass_att_away has 20 (100.0%) missing valuesMissing
pass_yards_away has 20 (100.0%) missing valuesMissing
pass_comp_home has 20 (100.0%) missing valuesMissing
pass_att_home has 20 (100.0%) missing valuesMissing
pass_yards_home has 20 (100.0%) missing valuesMissing
rush_att_away has 20 (100.0%) missing valuesMissing
rush_yards_away has 20 (100.0%) missing valuesMissing
rush_att_home has 20 (100.0%) missing valuesMissing
rush_yards_home has 20 (100.0%) missing valuesMissing
total_yards_away has 20 (100.0%) missing valuesMissing
total_yards_home has 20 (100.0%) missing valuesMissing
fum_away has 20 (100.0%) missing valuesMissing
fum_home has 20 (100.0%) missing valuesMissing
int_away has 20 (100.0%) missing valuesMissing
int_home has 20 (100.0%) missing valuesMissing
pen_num_away has 20 (100.0%) missing valuesMissing
pen_yards_away has 20 (100.0%) missing valuesMissing
pen_num_home has 20 (100.0%) missing valuesMissing
pen_yards_home has 20 (100.0%) missing valuesMissing
possession_away has 20 (100.0%) missing valuesMissing
possession_home has 20 (100.0%) missing valuesMissing
attendance has 1 (5.0%) missing valuesMissing
tv has 20 (100.0%) missing valuesMissing
rank_away is uniformly distributedUniform
rank_home is uniformly distributedUniform
away has unique valuesUnique
home has unique valuesUnique
home_nces_name has unique valuesUnique
home_lat has unique valuesUnique
home_lon has unique valuesUnique
away_nces_name has unique valuesUnique
away_lat has unique valuesUnique
away_lon has unique valuesUnique
game_lat has unique valuesUnique
game_lon has unique valuesUnique
weather_wind_speed_10m has unique valuesUnique
weather_wind_direction_10m has unique valuesUnique
first_downs_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
first_downs_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
third_down_comp_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
third_down_att_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
third_down_comp_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
third_down_att_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
fourth_down_comp_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
fourth_down_att_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
fourth_down_comp_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
fourth_down_att_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_comp_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_att_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_yards_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_comp_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_att_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_yards_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
rush_att_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
rush_yards_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
rush_att_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
rush_yards_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_yards_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_yards_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
fum_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
fum_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
int_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
int_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
pen_num_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
pen_yards_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
pen_num_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
pen_yards_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
possession_away is an unsupported type, check if it needs cleaning or further analysisUnsupported
possession_home is an unsupported type, check if it needs cleaning or further analysisUnsupported
tv is an unsupported type, check if it needs cleaning or further analysisUnsupported
score_home has 1 (5.0%) zerosZeros
q1_away has 2 (10.0%) zerosZeros
q3_home has 2 (10.0%) zerosZeros
q4_home has 2 (10.0%) zerosZeros

Reproduction

Analysis started2025-12-08 06:34:45.280019
Analysis finished2025-12-08 06:34:53.396414
Duration8.12 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

season
Categorical

Constant 

Distinct1
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2002
20 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters80
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2002
2nd row2002
3rd row2002
4th row2002
5th row2002

Common Values

ValueCountFrequency (%)
200220
100.0%

Length

2025-12-07T22:34:53.425136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:53.447015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
200220
100.0%

Most occurring characters

ValueCountFrequency (%)
240
50.0%
040
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)80
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
240
50.0%
040
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
240
50.0%
040
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
240
50.0%
040
50.0%

week
Categorical

High correlation 

Distinct2
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2.0
13 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.013
65.0%
1.07
35.0%

Length

2025-12-07T22:34:53.469021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:53.488424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.013
65.0%
1.07
35.0%

Most occurring characters

ValueCountFrequency (%)
.20
33.3%
020
33.3%
213
21.7%
17
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.20
33.3%
020
33.3%
213
21.7%
17
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.20
33.3%
020
33.3%
213
21.7%
17
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.20
33.3%
020
33.3%
213
21.7%
17
 
11.7%

date
Date

Distinct6
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size292.0 B
Minimum2002-08-22 00:00:00
Maximum2002-08-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-07T22:34:53.507587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:53.538262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

time_et
Date

Distinct9
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size292.0 B
Minimum2025-12-07 14:30:00
Maximum2025-12-07 22:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-07T22:34:53.566489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:53.594195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

game_type
Categorical

Constant 

Distinct1
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
regular
20 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular20
100.0%

Length

2025-12-07T22:34:53.628411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:53.644609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
regular20
100.0%

Most occurring characters

ValueCountFrequency (%)
r40
28.6%
e20
14.3%
g20
14.3%
u20
14.3%
l20
14.3%
a20
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r40
28.6%
e20
14.3%
g20
14.3%
u20
14.3%
l20
14.3%
a20
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r40
28.6%
e20
14.3%
g20
14.3%
u20
14.3%
l20
14.3%
a20
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r40
28.6%
e20
14.3%
g20
14.3%
u20
14.3%
l20
14.3%
a20
14.3%

away
Text

Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-12-07T22:34:53.684271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length14.5
Mean length12.15
Min length6

Characters and Unicode

Total characters243
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st rowColorado State
2nd rowFresno State
3rd rowTexas Tech
4th rowNew Mexico
5th rowArizona State
ValueCountFrequency (%)
state8
20.5%
tech2
 
5.1%
new2
 
5.1%
florida2
 
5.1%
colorado1
 
2.6%
texas1
 
2.6%
mexico1
 
2.6%
arizona1
 
2.6%
arkansas1
 
2.6%
slo1
 
2.6%
Other values (19)19
48.7%
2025-12-07T22:34:53.770422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a27
 
11.1%
e27
 
11.1%
t22
 
9.1%
19
 
7.8%
o15
 
6.2%
n13
 
5.3%
S12
 
4.9%
s11
 
4.5%
r10
 
4.1%
i10
 
4.1%
Other values (29)77
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a27
 
11.1%
e27
 
11.1%
t22
 
9.1%
19
 
7.8%
o15
 
6.2%
n13
 
5.3%
S12
 
4.9%
s11
 
4.5%
r10
 
4.1%
i10
 
4.1%
Other values (29)77
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a27
 
11.1%
e27
 
11.1%
t22
 
9.1%
19
 
7.8%
o15
 
6.2%
n13
 
5.3%
S12
 
4.9%
s11
 
4.5%
r10
 
4.1%
i10
 
4.1%
Other values (29)77
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a27
 
11.1%
e27
 
11.1%
t22
 
9.1%
19
 
7.8%
o15
 
6.2%
n13
 
5.3%
S12
 
4.9%
s11
 
4.5%
r10
 
4.1%
i10
 
4.1%
Other values (29)77
31.7%

home
Text

Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-12-07T22:34:53.824834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length11
Mean length9.6
Min length3

Characters and Unicode

Total characters192
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st rowVirginia
2nd rowWisconsin
3rd rowOhio State
4th rowNC State
5th rowNebraska
ValueCountFrequency (%)
state6
19.4%
virginia2
 
6.5%
michigan2
 
6.5%
green1
 
3.2%
ohio1
 
3.2%
nc1
 
3.2%
nebraska1
 
3.2%
iowa1
 
3.2%
tech1
 
3.2%
toledo1
 
3.2%
Other values (14)14
45.2%
2025-12-07T22:34:53.909527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e20
 
10.4%
i16
 
8.3%
t16
 
8.3%
a16
 
8.3%
n15
 
7.8%
11
 
5.7%
o11
 
5.7%
r10
 
5.2%
l8
 
4.2%
s7
 
3.6%
Other values (26)62
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e20
 
10.4%
i16
 
8.3%
t16
 
8.3%
a16
 
8.3%
n15
 
7.8%
11
 
5.7%
o11
 
5.7%
r10
 
5.2%
l8
 
4.2%
s7
 
3.6%
Other values (26)62
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e20
 
10.4%
i16
 
8.3%
t16
 
8.3%
a16
 
8.3%
n15
 
7.8%
11
 
5.7%
o11
 
5.7%
r10
 
5.2%
l8
 
4.2%
s7
 
3.6%
Other values (26)62
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e20
 
10.4%
i16
 
8.3%
t16
 
8.3%
a16
 
8.3%
n15
 
7.8%
11
 
5.7%
o11
 
5.7%
r10
 
5.2%
l8
 
4.2%
s7
 
3.6%
Other values (26)62
32.3%

rank_away
Categorical

High correlation  Missing  Uniform 

Distinct2
Distinct (%)100.0%
Missing18
Missing (%)90.0%
Memory size1.2 KiB
3.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row3.0
2nd row1.0

Common Values

ValueCountFrequency (%)
3.01
 
5.0%
1.01
 
5.0%
(Missing)18
90.0%

Length

2025-12-07T22:34:53.942806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:53.960796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.01
50.0%
1.01
50.0%

Most occurring characters

ValueCountFrequency (%)
.2
33.3%
02
33.3%
31
16.7%
11
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.2
33.3%
02
33.3%
31
16.7%
11
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.2
33.3%
02
33.3%
31
16.7%
11
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.2
33.3%
02
33.3%
31
16.7%
11
16.7%

rank_home
Categorical

High correlation  Missing  Uniform 

Distinct4
Distinct (%)100.0%
Missing16
Missing (%)80.0%
Memory size1.2 KiB
25.0
13.0
10.0
16.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row25.0
2nd row13.0
3rd row10.0
4th row16.0

Common Values

ValueCountFrequency (%)
25.01
 
5.0%
13.01
 
5.0%
10.01
 
5.0%
16.01
 
5.0%
(Missing)16
80.0%

Length

2025-12-07T22:34:53.988958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.013357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
25.01
25.0%
13.01
25.0%
10.01
25.0%
16.01
25.0%

Most occurring characters

ValueCountFrequency (%)
05
31.2%
.4
25.0%
13
18.8%
21
 
6.2%
51
 
6.2%
31
 
6.2%
61
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05
31.2%
.4
25.0%
13
18.8%
21
 
6.2%
51
 
6.2%
31
 
6.2%
61
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05
31.2%
.4
25.0%
13
18.8%
21
 
6.2%
51
 
6.2%
31
 
6.2%
61
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05
31.2%
.4
25.0%
13
18.8%
21
 
6.2%
51
 
6.2%
31
 
6.2%
61
 
6.2%

conf_away
Text

Missing 

Distinct7
Distinct (%)63.6%
Missing9
Missing (%)45.0%
Memory size1008.0 B
2025-12-07T22:34:54.049419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.4545455
Min length3

Characters and Unicode

Total characters49
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)36.4%

Sample

1st rowmwc
2nd rowwac
3rd rowbig12
4th rowmwc
5th rowpac12
ValueCountFrequency (%)
mwc3
27.3%
big122
18.2%
acc2
18.2%
wac1
 
9.1%
pac121
 
9.1%
sun-belt1
 
9.1%
big-east1
 
9.1%
2025-12-07T22:34:54.116824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c9
18.4%
a5
10.2%
b4
 
8.2%
w4
 
8.2%
13
 
6.1%
23
 
6.1%
m3
 
6.1%
g3
 
6.1%
i3
 
6.1%
s2
 
4.1%
Other values (7)10
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)49
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c9
18.4%
a5
10.2%
b4
 
8.2%
w4
 
8.2%
13
 
6.1%
23
 
6.1%
m3
 
6.1%
g3
 
6.1%
i3
 
6.1%
s2
 
4.1%
Other values (7)10
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c9
18.4%
a5
10.2%
b4
 
8.2%
w4
 
8.2%
13
 
6.1%
23
 
6.1%
m3
 
6.1%
g3
 
6.1%
i3
 
6.1%
s2
 
4.1%
Other values (7)10
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c9
18.4%
a5
10.2%
b4
 
8.2%
w4
 
8.2%
13
 
6.1%
23
 
6.1%
m3
 
6.1%
g3
 
6.1%
i3
 
6.1%
s2
 
4.1%
Other values (7)10
20.4%

conf_home
Categorical

High correlation 

Distinct9
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
mac
acc
big10
big12
big-east
Other values (4)

Length

Max length8
Median length3
Mean length4
Min length3

Characters and Unicode

Total characters80
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)15.0%

Sample

1st rowacc
2nd rowbig10
3rd rowbig10
4th rowacc
5th rowbig12

Common Values

ValueCountFrequency (%)
mac7
35.0%
acc2
 
10.0%
big102
 
10.0%
big122
 
10.0%
big-east2
 
10.0%
wac2
 
10.0%
ind1
 
5.0%
mwc1
 
5.0%
pac121
 
5.0%

Length

2025-12-07T22:34:54.150057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.176970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mac7
35.0%
acc2
 
10.0%
big102
 
10.0%
big122
 
10.0%
big-east2
 
10.0%
wac2
 
10.0%
ind1
 
5.0%
mwc1
 
5.0%
pac121
 
5.0%

Most occurring characters

ValueCountFrequency (%)
c15
18.8%
a14
17.5%
m8
10.0%
i7
8.8%
b6
 
7.5%
g6
 
7.5%
15
 
6.2%
w3
 
3.8%
23
 
3.8%
-2
 
2.5%
Other values (7)11
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)80
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c15
18.8%
a14
17.5%
m8
10.0%
i7
8.8%
b6
 
7.5%
g6
 
7.5%
15
 
6.2%
w3
 
3.8%
23
 
3.8%
-2
 
2.5%
Other values (7)11
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c15
18.8%
a14
17.5%
m8
10.0%
i7
8.8%
b6
 
7.5%
g6
 
7.5%
15
 
6.2%
w3
 
3.8%
23
 
3.8%
-2
 
2.5%
Other values (7)11
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c15
18.8%
a14
17.5%
m8
10.0%
i7
8.8%
b6
 
7.5%
g6
 
7.5%
15
 
6.2%
w3
 
3.8%
23
 
3.8%
-2
 
2.5%
Other values (7)11
13.8%

neutral
Boolean

Constant 

Distinct1
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size152.0 B
False
20 
ValueCountFrequency (%)
False20
100.0%
2025-12-07T22:34:54.201820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

score_away
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19
Minimum7
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:54.220782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q110
median15
Q324.5
95-th percentile38.15
Maximum41
Range34
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation11.973655
Coefficient of variation (CV)0.63019238
Kurtosis-0.90104075
Mean19
Median Absolute Deviation (MAD)6
Skewness0.78958628
Sum380
Variance143.36842
MonotonicityNot monotonic
2025-12-07T22:34:54.243962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
104
20.0%
74
20.0%
213
15.0%
142
10.0%
372
10.0%
351
 
5.0%
381
 
5.0%
161
 
5.0%
171
 
5.0%
411
 
5.0%
ValueCountFrequency (%)
74
20.0%
104
20.0%
142
10.0%
161
 
5.0%
171
 
5.0%
213
15.0%
351
 
5.0%
372
10.0%
381
 
5.0%
411
 
5.0%
ValueCountFrequency (%)
411
 
5.0%
381
 
5.0%
372
10.0%
351
 
5.0%
213
15.0%
171
 
5.0%
161
 
5.0%
142
10.0%
104
20.0%
74
20.0%

score_home
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.7
Minimum0
Maximum63
Zeros1
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:54.267758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.2
Q130.5
median37.5
Q345.75
95-th percentile51.6
Maximum63
Range63
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation13.951419
Coefficient of variation (CV)0.38014767
Kurtosis1.4872296
Mean36.7
Median Absolute Deviation (MAD)8
Skewness-0.76702057
Sum734
Variance194.64211
MonotonicityNot monotonic
2025-12-07T22:34:54.294619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
344
20.0%
482
 
10.0%
422
 
10.0%
291
 
5.0%
231
 
5.0%
451
 
5.0%
311
 
5.0%
631
 
5.0%
441
 
5.0%
511
 
5.0%
Other values (5)5
25.0%
ValueCountFrequency (%)
01
 
5.0%
161
 
5.0%
231
 
5.0%
261
 
5.0%
291
 
5.0%
311
 
5.0%
344
20.0%
411
 
5.0%
422
10.0%
441
 
5.0%
ValueCountFrequency (%)
631
 
5.0%
511
 
5.0%
491
 
5.0%
482
10.0%
451
 
5.0%
441
 
5.0%
422
10.0%
411
 
5.0%
344
20.0%
311
 
5.0%

q1_away
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)50.0%
Missing8
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum0
Maximum17
Zeros2
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:54.318933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q37
95-th percentile15.35
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.0181489
Coefficient of variation (CV)0.7720229
Kurtosis0.73519704
Mean6.5
Median Absolute Deviation (MAD)2.5
Skewness0.81581135
Sum78
Variance25.181818
MonotonicityNot monotonic
2025-12-07T22:34:54.343945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
75
25.0%
02
 
10.0%
32
 
10.0%
61
 
5.0%
171
 
5.0%
141
 
5.0%
(Missing)8
40.0%
ValueCountFrequency (%)
02
 
10.0%
32
 
10.0%
61
 
5.0%
75
25.0%
141
 
5.0%
171
 
5.0%
ValueCountFrequency (%)
171
 
5.0%
141
 
5.0%
75
25.0%
61
 
5.0%
32
 
10.0%
02
 
10.0%

q2_away
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
0.0
7.0
13.0
14.0
3.0

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Characters and Unicode

Total characters38
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)25.0%

Sample

1st row13.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07
35.0%
7.02
 
10.0%
13.01
 
5.0%
14.01
 
5.0%
3.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.374405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.401097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.07
58.3%
7.02
 
16.7%
13.01
 
8.3%
14.01
 
8.3%
3.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
019
50.0%
.12
31.6%
72
 
5.3%
12
 
5.3%
32
 
5.3%
41
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019
50.0%
.12
31.6%
72
 
5.3%
12
 
5.3%
32
 
5.3%
41
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019
50.0%
.12
31.6%
72
 
5.3%
12
 
5.3%
32
 
5.3%
41
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019
50.0%
.12
31.6%
72
 
5.3%
12
 
5.3%
32
 
5.3%
41
 
2.6%

q3_away
Categorical

High correlation  Missing 

Distinct4
Distinct (%)33.3%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
7.0
0.0
14.0
3.0

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Characters and Unicode

Total characters38
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)8.3%

Sample

1st row3.0
2nd row7.0
3rd row0.0
4th row7.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.05
25.0%
0.04
20.0%
14.02
 
10.0%
3.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.433429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.455366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.05
41.7%
0.04
33.3%
14.02
 
16.7%
3.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
016
42.1%
.12
31.6%
75
 
13.2%
12
 
5.3%
42
 
5.3%
31
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016
42.1%
.12
31.6%
75
 
13.2%
12
 
5.3%
42
 
5.3%
31
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016
42.1%
.12
31.6%
75
 
13.2%
12
 
5.3%
42
 
5.3%
31
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016
42.1%
.12
31.6%
75
 
13.2%
12
 
5.3%
42
 
5.3%
31
 
2.6%

q4_away
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
0.0
7.0
13.0
14.0
20.0

Length

Max length4
Median length3
Mean length3.25
Min length3

Characters and Unicode

Total characters39
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)25.0%

Sample

1st row13.0
2nd row7.0
3rd row14.0
4th row7.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06
30.0%
7.03
 
15.0%
13.01
 
5.0%
14.01
 
5.0%
20.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.482953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.505386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.06
50.0%
7.03
25.0%
13.01
 
8.3%
14.01
 
8.3%
20.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
019
48.7%
.12
30.8%
73
 
7.7%
12
 
5.1%
31
 
2.6%
41
 
2.6%
21
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019
48.7%
.12
30.8%
73
 
7.7%
12
 
5.1%
31
 
2.6%
41
 
2.6%
21
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019
48.7%
.12
30.8%
73
 
7.7%
12
 
5.1%
31
 
2.6%
41
 
2.6%
21
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019
48.7%
.12
30.8%
73
 
7.7%
12
 
5.1%
31
 
2.6%
41
 
2.6%
21
 
2.6%

ot_away
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)16.7%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
0.0
11 
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.011
55.0%
6.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.538144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.558074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.011
91.7%
6.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
023
63.9%
.12
33.3%
61
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
023
63.9%
.12
33.3%
61
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
023
63.9%
.12
33.3%
61
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
023
63.9%
.12
33.3%
61
 
2.8%

q1_home
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
0.0
7.0
14.0
3.0
35.0

Length

Max length4
Median length3
Mean length3.25
Min length3

Characters and Unicode

Total characters39
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row14.0
4th row7.0
5th row3.0

Common Values

ValueCountFrequency (%)
0.04
20.0%
7.03
 
15.0%
14.02
 
10.0%
3.02
 
10.0%
35.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.581652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.609742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04
33.3%
7.03
25.0%
14.02
16.7%
3.02
16.7%
35.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
016
41.0%
.12
30.8%
73
 
7.7%
33
 
7.7%
12
 
5.1%
42
 
5.1%
51
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016
41.0%
.12
30.8%
73
 
7.7%
33
 
7.7%
12
 
5.1%
42
 
5.1%
51
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016
41.0%
.12
30.8%
73
 
7.7%
33
 
7.7%
12
 
5.1%
42
 
5.1%
51
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016
41.0%
.12
30.8%
73
 
7.7%
33
 
7.7%
12
 
5.1%
42
 
5.1%
51
 
2.6%

q2_home
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
7.0
10.0
14.0
21.0
0.0

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Characters and Unicode

Total characters44
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)8.3%

Sample

1st row7.0
2nd row10.0
3rd row7.0
4th row14.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.03
 
15.0%
10.03
 
15.0%
14.03
 
15.0%
21.02
 
10.0%
0.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.640388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.662478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.03
25.0%
10.03
25.0%
14.03
25.0%
21.02
16.7%
0.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
016
36.4%
.12
27.3%
18
18.2%
73
 
6.8%
43
 
6.8%
22
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)44
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016
36.4%
.12
27.3%
18
18.2%
73
 
6.8%
43
 
6.8%
22
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016
36.4%
.12
27.3%
18
18.2%
73
 
6.8%
43
 
6.8%
22
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016
36.4%
.12
27.3%
18
18.2%
73
 
6.8%
43
 
6.8%
22
 
4.5%

q3_home
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)50.0%
Missing8
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean7.3333333
Minimum0
Maximum17
Zeros2
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:54.685639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median7
Q38.75
95-th percentile15.35
Maximum17
Range17
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation5.3314391
Coefficient of variation (CV)0.72701442
Kurtosis-0.36169912
Mean7.3333333
Median Absolute Deviation (MAD)2.5
Skewness0.44696046
Sum88
Variance28.424242
MonotonicityNot monotonic
2025-12-07T22:34:54.709195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
74
20.0%
142
 
10.0%
62
 
10.0%
02
 
10.0%
171
 
5.0%
31
 
5.0%
(Missing)8
40.0%
ValueCountFrequency (%)
02
10.0%
31
 
5.0%
62
10.0%
74
20.0%
142
10.0%
171
 
5.0%
ValueCountFrequency (%)
171
 
5.0%
142
10.0%
74
20.0%
62
10.0%
31
 
5.0%
02
10.0%

q4_home
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8
Distinct (%)66.7%
Missing8
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean10
Minimum0
Maximum24
Zeros2
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:54.733014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.75
median9.5
Q314
95-th percentile19.05
Maximum24
Range24
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation6.7957206
Coefficient of variation (CV)0.67957206
Kurtosis0.4177031
Mean10
Median Absolute Deviation (MAD)4.5
Skewness0.32535529
Sum120
Variance46.181818
MonotonicityNot monotonic
2025-12-07T22:34:54.755614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
143
 
15.0%
72
 
10.0%
02
 
10.0%
81
 
5.0%
61
 
5.0%
241
 
5.0%
151
 
5.0%
111
 
5.0%
(Missing)8
40.0%
ValueCountFrequency (%)
02
10.0%
61
 
5.0%
72
10.0%
81
 
5.0%
111
 
5.0%
143
15.0%
151
 
5.0%
241
 
5.0%
ValueCountFrequency (%)
241
 
5.0%
151
 
5.0%
143
15.0%
111
 
5.0%
81
 
5.0%
72
10.0%
61
 
5.0%
02
10.0%

ot_home
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)16.7%
Missing8
Missing (%)40.0%
Memory size1.2 KiB
0.0
11 
7.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.011
55.0%
7.01
 
5.0%
(Missing)8
40.0%

Length

2025-12-07T22:34:54.786643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:54.895999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.011
91.7%
7.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
023
63.9%
.12
33.3%
71
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
023
63.9%
.12
33.3%
71
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
023
63.9%
.12
33.3%
71
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
023
63.9%
.12
33.3%
71
 
2.8%

first_downs_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

first_downs_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

third_down_comp_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

third_down_att_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

third_down_comp_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

third_down_att_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

fourth_down_comp_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

fourth_down_att_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

fourth_down_comp_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

fourth_down_att_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pass_comp_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pass_att_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pass_yards_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pass_comp_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pass_att_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pass_yards_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

rush_att_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

rush_yards_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

rush_att_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

rush_yards_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

total_yards_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

total_yards_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

fum_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

fum_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

int_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

int_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pen_num_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pen_yards_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pen_num_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

pen_yards_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

possession_away
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

possession_home
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

attendance
Real number (ℝ)

High correlation  Missing 

Distinct19
Distinct (%)100.0%
Missing1
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean41654.842
Minimum15329
Maximum100037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:54.918372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15329
5-th percentile15659.3
Q120378
median39111
Q356126
95-th percentile80004.8
Maximum100037
Range84708
Interquartile range (IQR)35748

Descriptive statistics

Standard deviation25139.209
Coefficient of variation (CV)0.60351228
Kurtosis-0.19874849
Mean41654.842
Median Absolute Deviation (MAD)18009
Skewness0.80336433
Sum791442
Variance6.3197982 × 108
MonotonicityNot monotonic
2025-12-07T22:34:54.945500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
571201
 
5.0%
751361
 
5.0%
391111
 
5.0%
196531
 
5.0%
282681
 
5.0%
188261
 
5.0%
211031
 
5.0%
656121
 
5.0%
156961
 
5.0%
220741
 
5.0%
Other values (9)9
45.0%
ValueCountFrequency (%)
153291
5.0%
156961
5.0%
160731
5.0%
188261
5.0%
196531
5.0%
211031
5.0%
220741
5.0%
230741
5.0%
282681
5.0%
391111
5.0%
ValueCountFrequency (%)
1000371
5.0%
777791
5.0%
751361
5.0%
656121
5.0%
571201
5.0%
551321
5.0%
540161
5.0%
470181
5.0%
403851
5.0%
391111
5.0%

tv
Unsupported

Missing  Rejected  Unsupported 

Missing20
Missing (%)100.0%
Memory size292.0 B

home_nces_name
Text

Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-12-07T22:34:55.005803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length31
Mean length29.3
Min length17

Characters and Unicode

Total characters586
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st rowUniversity of Virginia-Main Campus
2nd rowUniversity of Wisconsin-Madison
3rd rowOhio State University-Main Campus
4th rowNorth Carolina State University at Raleigh
5th rowUniversity of Nebraska-Lincoln
ValueCountFrequency (%)
university16
22.2%
state8
 
11.1%
of6
 
8.3%
campus3
 
4.2%
at3
 
4.2%
michigan2
 
2.8%
kent2
 
2.8%
university-main2
 
2.8%
nebraska-lincoln1
 
1.4%
iowa1
 
1.4%
Other values (28)28
38.9%
2025-12-07T22:34:55.099879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i69
11.8%
52
 
8.9%
t50
 
8.5%
n48
 
8.2%
e45
 
7.7%
a36
 
6.1%
r36
 
6.1%
s32
 
5.5%
o28
 
4.8%
y21
 
3.6%
Other values (32)169
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i69
11.8%
52
 
8.9%
t50
 
8.5%
n48
 
8.2%
e45
 
7.7%
a36
 
6.1%
r36
 
6.1%
s32
 
5.5%
o28
 
4.8%
y21
 
3.6%
Other values (32)169
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i69
11.8%
52
 
8.9%
t50
 
8.5%
n48
 
8.2%
e45
 
7.7%
a36
 
6.1%
r36
 
6.1%
s32
 
5.5%
o28
 
4.8%
y21
 
3.6%
Other values (32)169
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i69
11.8%
52
 
8.9%
t50
 
8.5%
n48
 
8.2%
e45
 
7.7%
a36
 
6.1%
r36
 
6.1%
s32
 
5.5%
o28
 
4.8%
y21
 
3.6%
Other values (32)169
28.8%

home_lat
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.888978
Minimum28.061458
Maximum44.56395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.129163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28.061458
5-th percentile35.398928
Q137.834002
median40.982125
Q342.090207
95-th percentile43.63984
Maximum44.56395
Range16.502492
Interquartile range (IQR)4.2562057

Descriptive statistics

Standard deviation3.7591851
Coefficient of variation (CV)0.094241198
Kurtosis4.1120059
Mean39.888978
Median Absolute Deviation (MAD)1.6594425
Skewness-1.7294947
Sum797.77956
Variance14.131473
MonotonicityNot monotonic
2025-12-07T22:34:55.160216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
38.0356651
 
5.0%
43.0754091
 
5.0%
36.8111541
 
5.0%
44.563951
 
5.0%
41.9338691
 
5.0%
42.2821941
 
5.0%
43.5912031
 
5.0%
43.0009421
 
5.0%
40.2508511
 
5.0%
41.3755131
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
28.0614581
5.0%
35.7851111
5.0%
36.1523241
5.0%
36.8111541
5.0%
37.2290121
5.0%
38.0356651
5.0%
39.9805461
5.0%
39.9993241
5.0%
40.2508511
5.0%
40.8175981
5.0%
ValueCountFrequency (%)
44.563951
5.0%
43.5912031
5.0%
43.0754091
5.0%
43.0009421
5.0%
42.2821941
5.0%
42.0262121
5.0%
41.9338691
5.0%
41.6605721
5.0%
41.3755131
5.0%
41.1466531
5.0%

home_lon
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-89.754861
Minimum-123.27472
Maximum-75.156859
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)100.0%
Memory size292.0 B
2025-12-07T22:34:55.187123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.27472
5-th percentile-119.92108
Q1-94.222863
median-84.208073
Q3-81.112819
95-th percentile-78.3361
Maximum-75.156859
Range48.117864
Interquartile range (IQR)13.110045

Descriptive statistics

Standard deviation13.692227
Coefficient of variation (CV)-0.15255137
Kurtosis1.4292924
Mean-89.754861
Median Absolute Deviation (MAD)5.30732
Skewness-1.4986108
Sum-1795.0972
Variance187.47708
MonotonicityNot monotonic
2025-12-07T22:34:55.213522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-78.5034281
 
5.0%
-89.4040981
 
5.0%
-119.7445691
 
5.0%
-123.2747231
 
5.0%
-88.7664281
 
5.0%
-85.6137591
 
5.0%
-84.775251
 
5.0%
-78.7894581
 
5.0%
-111.6492811
 
5.0%
-83.6408961
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
-123.2747231
5.0%
-119.7445691
5.0%
-111.6492811
5.0%
-96.7005081
5.0%
-95.9459411
5.0%
-93.6485041
5.0%
-89.4040981
5.0%
-88.7664281
5.0%
-85.6137591
5.0%
-84.775251
5.0%
ValueCountFrequency (%)
-75.1568591
5.0%
-78.5034281
5.0%
-78.6745171
5.0%
-78.7894581
5.0%
-80.4236751
5.0%
-81.3425331
5.0%
-82.4132321
5.0%
-83.0148531
5.0%
-83.6147141
5.0%
-83.6408961
5.0%

away_nces_name
Text

Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-12-07T22:34:55.269247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length35
Mean length29.75
Min length17

Characters and Unicode

Total characters595
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st rowColorado State University-Fort Collins
2nd rowCalifornia State University-Fresno
3rd rowTexas Tech University
4th rowUniversity of New Mexico-Main Campus
5th rowArizona State University Campus Immersion
ValueCountFrequency (%)
university17
23.9%
state9
 
12.7%
of4
 
5.6%
campus4
 
5.6%
florida2
 
2.8%
california2
 
2.8%
new2
 
2.8%
university-san1
 
1.4%
richmond1
 
1.4%
hampshire-main1
 
1.4%
Other values (28)28
39.4%
2025-12-07T22:34:55.366379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i62
 
10.4%
51
 
8.6%
e50
 
8.4%
t46
 
7.7%
n43
 
7.2%
a40
 
6.7%
s39
 
6.6%
r35
 
5.9%
o28
 
4.7%
y23
 
3.9%
Other values (34)178
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i62
 
10.4%
51
 
8.6%
e50
 
8.4%
t46
 
7.7%
n43
 
7.2%
a40
 
6.7%
s39
 
6.6%
r35
 
5.9%
o28
 
4.7%
y23
 
3.9%
Other values (34)178
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i62
 
10.4%
51
 
8.6%
e50
 
8.4%
t46
 
7.7%
n43
 
7.2%
a40
 
6.7%
s39
 
6.6%
r35
 
5.9%
o28
 
4.7%
y23
 
3.9%
Other values (34)178
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i62
 
10.4%
51
 
8.6%
e50
 
8.4%
t46
 
7.7%
n43
 
7.2%
a40
 
6.7%
s39
 
6.6%
r35
 
5.9%
o28
 
4.7%
y23
 
3.9%
Other values (34)178
29.9%

away_lat
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.000204
Minimum26.372421
Maximum43.135934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.399696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.372421
5-th percentile30.237339
Q133.542016
median35.987999
Q338.172902
95-th percentile43.044964
Maximum43.135934
Range16.763513
Interquartile range (IQR)4.6308858

Descriptive statistics

Standard deviation4.2134687
Coefficient of variation (CV)0.11704013
Kurtosis0.26869417
Mean36.000204
Median Absolute Deviation (MAD)2.487414
Skewness-0.2511038
Sum720.00408
Variance17.753318
MonotonicityNot monotonic
2025-12-07T22:34:55.429261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
40.5748051
 
5.0%
36.8111541
 
5.0%
32.775251
 
5.0%
37.7408451
 
5.0%
36.1336091
 
5.0%
39.4690731
 
5.0%
30.7147381
 
5.0%
40.6068221
 
5.0%
43.0401761
 
5.0%
36.1747541
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
26.3724211
5.0%
30.4407561
5.0%
30.7147381
5.0%
32.775251
5.0%
33.4177211
5.0%
33.5834481
5.0%
35.0838681
5.0%
35.2094071
5.0%
35.2995131
5.0%
35.8423881
5.0%
ValueCountFrequency (%)
43.1359341
5.0%
43.0401761
5.0%
40.6068221
5.0%
40.5748051
5.0%
39.4690731
5.0%
37.7408451
5.0%
37.5773931
5.0%
36.8111541
5.0%
36.1747541
5.0%
36.1336091
5.0%

away_lon
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-93.426354
Minimum-120.65731
Maximum-70.932465
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)100.0%
Memory size292.0 B
2025-12-07T22:34:55.460668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-120.65731
5-th percentile-119.79021
Q1-105.46559
median-89.043917
Q3-80.233829
95-th percentile-75.157847
Maximum-70.932465
Range49.724846
Interquartile range (IQR)25.231759

Descriptive statistics

Standard deviation15.801959
Coefficient of variation (CV)-0.16913814
Kurtosis-1.0952834
Mean-93.426354
Median Absolute Deviation (MAD)12.167988
Skewness-0.4328387
Sum-1868.5271
Variance249.70192
MonotonicityNot monotonic
2025-12-07T22:34:55.491230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-105.0807321
 
5.0%
-119.7445691
 
5.0%
-117.0712281
 
5.0%
-84.3007961
 
5.0%
-80.2774461
 
5.0%
-87.4078461
 
5.0%
-95.5462051
 
5.0%
-75.3802361
 
5.0%
-76.1369751
 
5.0%
-85.5040391
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
-120.6573111
5.0%
-119.7445691
5.0%
-117.0712281
5.0%
-111.9343831
5.0%
-106.6201551
5.0%
-105.0807321
5.0%
-101.8747831
5.0%
-97.4442111
5.0%
-95.5462051
5.0%
-90.6799881
5.0%
ValueCountFrequency (%)
-70.9324651
5.0%
-75.3802361
5.0%
-76.1369751
5.0%
-77.5388061
5.0%
-80.1029781
5.0%
-80.2774461
5.0%
-84.2919211
5.0%
-84.3007961
5.0%
-85.5040391
5.0%
-87.4078461
5.0%

game_lat
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.888978
Minimum28.061458
Maximum44.56395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.522058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28.061458
5-th percentile35.398928
Q137.834002
median40.982125
Q342.090207
95-th percentile43.63984
Maximum44.56395
Range16.502492
Interquartile range (IQR)4.2562057

Descriptive statistics

Standard deviation3.7591851
Coefficient of variation (CV)0.094241198
Kurtosis4.1120059
Mean39.888978
Median Absolute Deviation (MAD)1.6594425
Skewness-1.7294947
Sum797.77956
Variance14.131473
MonotonicityNot monotonic
2025-12-07T22:34:55.551825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
38.0356651
 
5.0%
43.0754091
 
5.0%
36.8111541
 
5.0%
44.563951
 
5.0%
41.9338691
 
5.0%
42.2821941
 
5.0%
43.5912031
 
5.0%
43.0009421
 
5.0%
40.2508511
 
5.0%
41.3755131
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
28.0614581
5.0%
35.7851111
5.0%
36.1523241
5.0%
36.8111541
5.0%
37.2290121
5.0%
38.0356651
5.0%
39.9805461
5.0%
39.9993241
5.0%
40.2508511
5.0%
40.8175981
5.0%
ValueCountFrequency (%)
44.563951
5.0%
43.5912031
5.0%
43.0754091
5.0%
43.0009421
5.0%
42.2821941
5.0%
42.0262121
5.0%
41.9338691
5.0%
41.6605721
5.0%
41.3755131
5.0%
41.1466531
5.0%

game_lon
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-89.754861
Minimum-123.27472
Maximum-75.156859
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)100.0%
Memory size292.0 B
2025-12-07T22:34:55.583879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.27472
5-th percentile-119.92108
Q1-94.222863
median-84.208073
Q3-81.112819
95-th percentile-78.3361
Maximum-75.156859
Range48.117864
Interquartile range (IQR)13.110045

Descriptive statistics

Standard deviation13.692227
Coefficient of variation (CV)-0.15255137
Kurtosis1.4292924
Mean-89.754861
Median Absolute Deviation (MAD)5.30732
Skewness-1.4986108
Sum-1795.0972
Variance187.47708
MonotonicityNot monotonic
2025-12-07T22:34:55.612953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-78.5034281
 
5.0%
-89.4040981
 
5.0%
-119.7445691
 
5.0%
-123.2747231
 
5.0%
-88.7664281
 
5.0%
-85.6137591
 
5.0%
-84.775251
 
5.0%
-78.7894581
 
5.0%
-111.6492811
 
5.0%
-83.6408961
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
-123.2747231
5.0%
-119.7445691
5.0%
-111.6492811
5.0%
-96.7005081
5.0%
-95.9459411
5.0%
-93.6485041
5.0%
-89.4040981
5.0%
-88.7664281
5.0%
-85.6137591
5.0%
-84.775251
5.0%
ValueCountFrequency (%)
-75.1568591
5.0%
-78.5034281
5.0%
-78.6745171
5.0%
-78.7894581
5.0%
-80.4236751
5.0%
-81.3425331
5.0%
-82.4132321
5.0%
-83.0148531
5.0%
-83.6147141
5.0%
-83.6408961
5.0%

processed_time
Date

Constant 

Distinct1
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size292.0 B
Minimum2025-12-07 22:33:30
Maximum2025-12-07 22:33:30
Invalid dates0
Invalid dates (%)0.0%
2025-12-07T22:34:55.636382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:55.661915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

weather_temperature_2m
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.25
Minimum16.7
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.688826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16.7
5-th percentile20.5
Q124.025
median24.5
Q326.6
95-th percentile31.345
Maximum36
Range19.3
Interquartile range (IQR)2.575

Descriptive statistics

Standard deviation3.9564072
Coefficient of variation (CV)0.15668939
Kurtosis2.5863062
Mean25.25
Median Absolute Deviation (MAD)1.7
Skewness0.69763808
Sum505
Variance15.653158
MonotonicityNot monotonic
2025-12-07T22:34:55.715062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
24.53
15.0%
26.62
 
10.0%
31.11
 
5.0%
28.81
 
5.0%
20.71
 
5.0%
24.21
 
5.0%
24.41
 
5.0%
21.91
 
5.0%
24.61
 
5.0%
22.41
 
5.0%
Other values (7)7
35.0%
ValueCountFrequency (%)
16.71
 
5.0%
20.71
 
5.0%
21.91
 
5.0%
22.41
 
5.0%
23.81
 
5.0%
24.11
 
5.0%
24.21
 
5.0%
24.41
 
5.0%
24.53
15.0%
24.61
 
5.0%
ValueCountFrequency (%)
361
 
5.0%
31.11
 
5.0%
28.81
 
5.0%
28.51
 
5.0%
26.62
10.0%
25.81
 
5.0%
25.31
 
5.0%
24.61
 
5.0%
24.53
15.0%
24.41
 
5.0%

weather_humidity_2m
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58
Minimum30
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.739621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile34.75
Q150.5
median58
Q366.75
95-th percentile74.05
Maximum94
Range64
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation14.356367
Coefficient of variation (CV)0.24752356
Kurtosis1.1615443
Mean58
Median Absolute Deviation (MAD)8.5
Skewness0.35278368
Sum1160
Variance206.10526
MonotonicityNot monotonic
2025-12-07T22:34:55.767989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
522
 
10.0%
582
 
10.0%
602
 
10.0%
462
 
10.0%
731
 
5.0%
661
 
5.0%
351
 
5.0%
711
 
5.0%
691
 
5.0%
511
 
5.0%
Other values (6)6
30.0%
ValueCountFrequency (%)
301
5.0%
351
5.0%
462
10.0%
491
5.0%
511
5.0%
522
10.0%
551
5.0%
582
10.0%
602
10.0%
631
5.0%
ValueCountFrequency (%)
941
5.0%
731
5.0%
721
5.0%
711
5.0%
691
5.0%
661
5.0%
631
5.0%
602
10.0%
582
10.0%
551
5.0%

weather_wind_speed_10m
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.445
Minimum1.9
Maximum19.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.792391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2.85
Q16.475
median8.3
Q312.625
95-th percentile17.39
Maximum19.1
Range17.2
Interquartile range (IQR)6.15

Descriptive statistics

Standard deviation4.9563861
Coefficient of variation (CV)0.52476295
Kurtosis-0.68973979
Mean9.445
Median Absolute Deviation (MAD)3.9
Skewness0.41582638
Sum188.9
Variance24.565763
MonotonicityNot monotonic
2025-12-07T22:34:55.819424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10.21
 
5.0%
1.91
 
5.0%
2.91
 
5.0%
7.41
 
5.0%
81
 
5.0%
5.81
 
5.0%
3.71
 
5.0%
8.41
 
5.0%
19.11
 
5.0%
16.21
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
1.91
5.0%
2.91
5.0%
3.71
5.0%
41
5.0%
5.81
5.0%
6.71
5.0%
6.81
5.0%
7.41
5.0%
81
5.0%
8.21
5.0%
ValueCountFrequency (%)
19.11
5.0%
17.31
5.0%
16.21
5.0%
15.81
5.0%
12.71
5.0%
12.61
5.0%
11.81
5.0%
10.21
5.0%
9.41
5.0%
8.41
5.0%

weather_wind_direction_10m
Real number (ℝ)

High correlation  Unique 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.1
Minimum12
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size292.0 B
2025-12-07T22:34:55.846134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile60.45
Q180.5
median122.5
Q3255
95-th percentile338.15
Maximum360
Range348
Interquartile range (IQR)174.5

Descriptive statistics

Standard deviation107.8961
Coefficient of variation (CV)0.64569779
Kurtosis-1.1295773
Mean167.1
Median Absolute Deviation (MAD)57.5
Skewness0.52053509
Sum3342
Variance11641.568
MonotonicityNot monotonic
2025-12-07T22:34:55.874261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2281
 
5.0%
1121
 
5.0%
2701
 
5.0%
3311
 
5.0%
631
 
5.0%
861
 
5.0%
1191
 
5.0%
831
 
5.0%
1781
 
5.0%
691
 
5.0%
Other values (10)10
50.0%
ValueCountFrequency (%)
121
5.0%
631
5.0%
671
5.0%
691
5.0%
731
5.0%
831
5.0%
861
5.0%
951
5.0%
1121
5.0%
1191
5.0%
ValueCountFrequency (%)
3601
5.0%
3371
5.0%
3311
5.0%
2991
5.0%
2701
5.0%
2501
5.0%
2281
5.0%
1841
5.0%
1781
5.0%
1261
5.0%

weather_precipitation
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
18 
0.3
 
1
0.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.018
90.0%
0.31
 
5.0%
0.21
 
5.0%

Length

2025-12-07T22:34:55.907653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:55.928004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.018
90.0%
0.31
 
5.0%
0.21
 
5.0%

Most occurring characters

ValueCountFrequency (%)
038
63.3%
.20
33.3%
31
 
1.7%
21
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
038
63.3%
.20
33.3%
31
 
1.7%
21
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
038
63.3%
.20
33.3%
31
 
1.7%
21
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
038
63.3%
.20
33.3%
31
 
1.7%
21
 
1.7%

weather_code
Categorical

High correlation 

Distinct5
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
0
3
51
1

Length

Max length2
Median length1
Mean length1.1
Min length1

Characters and Unicode

Total characters22
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.0%

Sample

1st row2
2nd row3
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
26
30.0%
06
30.0%
35
25.0%
512
 
10.0%
11
 
5.0%

Length

2025-12-07T22:34:55.953665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:34:55.976405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
26
30.0%
06
30.0%
35
25.0%
512
 
10.0%
11
 
5.0%

Most occurring characters

ValueCountFrequency (%)
26
27.3%
06
27.3%
35
22.7%
13
13.6%
52
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)22
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26
27.3%
06
27.3%
35
22.7%
13
13.6%
52
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26
27.3%
06
27.3%
35
22.7%
13
13.6%
52
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26
27.3%
06
27.3%
35
22.7%
13
13.6%
52
 
9.1%

Interactions

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2025-12-07T22:34:52.886914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.429278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.842348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.279662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.704315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.093791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.500757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.913623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.407279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.819553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.241420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.674941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.081562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.521508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.937606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.368960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.915991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.457878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.873830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.309596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.730659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.118426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.529295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.941885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.434510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.844854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.269118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.705555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.107918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.554753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.963648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.481438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.947744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.482750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.901180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.336753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.755902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.141500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.555876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.967577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.460684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.873851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.296025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.732755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.133567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.581554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.990144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.510041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.974285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.509847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:46.929672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.366847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:47.781212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.170274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:48.581327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.000293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.485571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:49.899150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.323105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:50.758926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.158361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:51.608013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.018442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:34:52.536645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T22:34:56.014002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
attendanceaway_lataway_lonconf_homegame_latgame_lonhome_lathome_lonot_awayot_homeq1_awayq1_homeq2_awayq2_homeq3_awayq3_homeq4_awayq4_homerank_awayrank_homescore_awayscore_homeweather_codeweather_humidity_2mweather_precipitationweather_temperature_2mweather_wind_direction_10mweather_wind_speed_10mweek
attendance1.000-0.174-0.5020.541-0.156-0.337-0.156-0.3370.0000.0000.0140.0000.0000.2550.0000.5050.3600.1461.0001.0000.3820.0620.425-0.0020.0000.5050.668-0.2110.804
away_lat-0.1741.0000.4180.1610.2810.1910.2810.1910.0000.0000.0910.0000.6070.0220.434-0.0860.1970.1131.0001.0000.005-0.0730.000-0.2050.000-0.440-0.4080.3500.000
away_lon-0.5020.4181.0000.0000.1250.1760.1250.1760.0000.0000.5090.2420.0000.4830.000-0.3650.2830.3781.0001.000-0.0800.1320.0000.0380.000-0.385-0.2590.3730.361
conf_home0.5410.1610.0001.0000.3750.4560.3750.4560.6320.6320.0000.0000.3500.1220.0000.0000.0000.4121.0001.0000.2060.3330.2400.3820.0000.3200.4840.1480.704
game_lat-0.1560.2810.1250.3751.000-0.3051.000-0.3050.0000.0000.7350.1970.3850.0000.329-0.0860.2350.4811.0001.0000.1530.0140.0000.3500.000-0.750-0.233-0.3070.000
game_lon-0.3370.1910.1760.456-0.3051.000-0.3051.0000.0000.000-0.2180.0000.5370.1640.0000.4830.000-0.3781.0001.000-0.222-0.0010.2940.0970.0000.050-0.2350.5130.000
home_lat-0.1560.2810.1250.3751.000-0.3051.000-0.3050.0000.0000.7350.1970.3850.0000.329-0.0860.2350.4811.0001.0000.1530.0140.0000.3500.000-0.750-0.233-0.3070.000
home_lon-0.3370.1910.1760.456-0.3051.000-0.3051.0000.0000.000-0.2180.0000.5370.1640.0000.4830.000-0.3781.0001.000-0.222-0.0010.2940.0970.0000.050-0.2350.5130.000
ot_away0.0000.0000.0000.6320.0000.0000.0000.0001.0000.3570.7750.0000.3160.0000.4470.0000.0000.7071.0001.0000.3160.0000.0000.0000.0000.0000.6320.0000.000
ot_home0.0000.0000.0000.6320.0000.0000.0000.0000.3571.0000.7750.0000.3160.0000.4470.0000.0000.7071.0001.0000.3160.0000.0000.0000.0000.0000.6320.0000.000
q1_away0.0140.0910.5090.0000.735-0.2180.735-0.2180.7750.7751.0000.0000.5640.0000.585-0.0890.1790.4141.0001.0000.493-0.1220.0000.4260.000-0.706-0.0690.0040.000
q1_home0.0000.0000.2420.0000.1970.0000.1970.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.5370.3070.3210.3160.0000.0000.0000.000
q2_away0.0000.6070.0000.3500.3850.5370.3850.5370.3160.3160.5640.0001.0000.0000.4440.3040.1650.3911.0001.0000.0000.1490.0000.0000.0000.0000.0000.0000.402
q2_home0.2550.0220.4830.1220.0000.1640.0000.1640.0000.0000.0000.0000.0001.0000.0000.5630.4130.0001.0001.0000.0000.3210.0000.3830.0000.5560.3020.0000.093
q3_away0.0000.4340.0000.0000.3290.0000.3290.0000.4470.4470.5850.0000.4440.0001.0000.0000.5060.5721.0001.0000.1250.0000.0000.0000.0000.3650.2960.0000.348
q3_home0.505-0.086-0.3650.000-0.0860.483-0.0860.4830.0000.000-0.0890.0000.3040.5630.0001.0000.3920.0161.0001.000-0.1410.2050.0000.3540.0000.140-0.2680.1720.185
q4_away0.3600.1970.2830.0000.2350.0000.2350.0000.0000.0000.1790.0000.1650.4130.5060.3921.0000.0701.0001.0000.3410.2200.2390.4240.0000.1220.2830.2180.444
q4_home0.1460.1130.3780.4120.481-0.3780.481-0.3780.7070.7070.4140.0000.3910.0000.5720.0160.0701.0001.0001.000-0.0980.2930.0000.2580.000-0.449-0.2080.1590.245
rank_away1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
rank_home1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
score_away0.3820.005-0.0800.2060.153-0.2220.153-0.2220.3160.3160.4930.0000.0000.0000.125-0.1410.341-0.0981.0001.0001.000-0.4150.000-0.0210.0000.1250.134-0.2000.000
score_home0.062-0.0730.1320.3330.014-0.0010.014-0.0010.0000.000-0.1220.5370.1490.3210.0000.2050.2200.2931.0001.000-0.4151.0000.0000.1880.0000.0050.1030.2290.225
weather_code0.4250.0000.0000.2400.0000.2940.0000.2940.0000.0000.0000.3070.0000.0000.0000.0000.2390.0001.0001.0000.0000.0001.0000.4080.5690.0000.4940.0000.582
weather_humidity_2m-0.002-0.2050.0380.3820.3500.0970.3500.0970.0000.0000.4260.3210.0000.3830.0000.3540.4240.2581.0001.000-0.0210.1880.4081.0000.685-0.405-0.145-0.1840.290
weather_precipitation0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3160.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.5690.6851.0000.5590.4520.0000.000
weather_temperature_2m0.505-0.440-0.3850.320-0.7500.050-0.7500.0500.0000.000-0.7060.0000.0000.5560.3650.1400.122-0.4491.0001.0000.1250.0050.000-0.4050.5591.0000.543-0.0540.169
weather_wind_direction_10m0.668-0.408-0.2590.484-0.233-0.235-0.233-0.2350.6320.632-0.0690.0000.0000.3020.296-0.2680.283-0.2081.0001.0000.1340.1030.494-0.1450.4520.5431.000-0.2260.474
weather_wind_speed_10m-0.2110.3500.3730.148-0.3070.513-0.3070.5130.0000.0000.0040.0000.0000.0000.0000.1720.2180.1591.0001.000-0.2000.2290.000-0.1840.000-0.054-0.2261.0000.000
week0.8040.0000.3610.7040.0000.0000.0000.0000.0000.0000.0000.0000.4020.0930.3480.1850.4440.2451.0001.0000.0000.2250.5820.2900.0000.1690.4740.0001.000

Missing values

2025-12-07T22:34:53.074945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T22:34:53.214262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-07T22:34:53.336223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seasonweekdatetime_etgame_typeawayhomerank_awayrank_homeconf_awayconf_homeneutralscore_awayscore_homeq1_awayq2_awayq3_awayq4_awayot_awayq1_homeq2_homeq3_homeq4_homeot_homefirst_downs_awayfirst_downs_homethird_down_comp_awaythird_down_att_awaythird_down_comp_homethird_down_att_homefourth_down_comp_awayfourth_down_att_awayfourth_down_comp_homefourth_down_att_homepass_comp_awaypass_att_awaypass_yards_awaypass_comp_homepass_att_homepass_yards_homerush_att_awayrush_yards_awayrush_att_homerush_yards_hometotal_yards_awaytotal_yards_homefum_awayfum_homeint_awayint_homepen_num_awaypen_yards_awaypen_num_homepen_yards_homepossession_awaypossession_homeattendancetvhome_nces_namehome_lathome_lonaway_nces_nameaway_lataway_longame_latgame_lonprocessed_timeweather_temperature_2mweather_humidity_2mweather_wind_speed_10mweather_wind_direction_10mweather_precipitationweather_code
020021.02002-08-227:30 PMregularColorado StateVirginiaNaNNaNmwcaccFalse35296.013.03.013.00.00.07.014.08.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN57120.0NaNUniversity of Virginia-Main Campus38.035665-78.503428Colorado State University-Fort Collins40.574805-105.08073238.035665-78.5034282025-12-07 22:33:3031.15210.22280.02
120021.02002-08-238:00 PMregularFresno StateWisconsinNaN25.0wacbig10False21237.00.07.07.00.00.010.07.06.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN75136.0NaNUniversity of Wisconsin-Madison43.075409-89.404098California State University-Fresno36.811154-119.74456943.075409-89.4040982025-12-07 22:33:3024.1731.91120.03
220021.02002-08-242:30 PMregularTexas TechOhio StateNaN13.0big12big10False21457.00.00.014.00.014.07.017.07.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100037.0NaNOhio State University-Main Campus39.999324-83.014853Texas Tech University33.583448-101.87478339.999324-83.0148532025-12-07 22:33:3025.86612.72990.01
320021.02002-08-244:30 PMregularNew MexicoNC StateNaNNaNmwcaccFalse14340.00.07.07.00.07.014.06.07.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN47018.0NaNNorth Carolina State University at Raleigh35.785111-78.674517University of New Mexico-Main Campus35.083868-106.62015535.785111-78.6745172025-12-07 22:33:3036.03512.62500.02
420021.02002-08-247:45 PMregularArizona StateNebraskaNaN10.0pac12big12False10483.00.07.00.00.03.07.014.024.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN77779.0NaNUniversity of Nebraska-Lincoln40.817598-96.700508Arizona State University Campus Immersion33.417721-111.93438340.817598-96.7005082025-12-07 22:33:3025.3714.0950.03
520021.02002-08-248:30 PMregularFlorida StateIowa State3.0NaNaccbig12False383117.014.00.07.00.00.014.03.014.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN55132.0NaNIowa State University42.026212-93.648504Florida State University30.440756-84.29192142.026212-93.6485042025-12-07 22:33:3024.5696.83600.03
620021.02002-08-252:30 PMregularArkansas StateVirginia TechNaN16.0sun-beltbig-eastFalse7630.00.07.00.00.035.021.07.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN54016.0NaNVirginia Polytechnic Institute and State University37.229012-80.423675Arkansas State University35.842388-90.67998837.229012-80.4236752025-12-07 22:33:3026.6518.23370.03
720022.02002-08-297:00 PMregularCal Poly SLOToledoNaNNaNNaNmacFalse1644NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23074.0NaNUniversity of Toledo41.660572-83.614714California Polytechnic State University-San Luis Obispo35.299513-120.65731141.660572-83.6147142025-12-07 22:33:3023.85815.8730.00
820022.02002-08-297:00 PMregularNew HampshireKent StateNaNNaNNaNmacFalse734NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN16073.0NaNKent State University at Kent41.146653-81.342533University of New Hampshire-Main Campus43.135934-70.93246541.146653-81.3425332025-12-07 22:33:3022.44911.8670.00
920022.02002-08-297:00 PMregularRichmondTempleNaNNaNNaNbig-eastFalse7347.00.00.00.00.03.010.07.014.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15329.0NaNTemple University39.980546-75.156859University of Richmond37.577393-77.53880639.980546-75.1568592025-12-07 22:33:3016.79417.3120.351
seasonweekdatetime_etgame_typeawayhomerank_awayrank_homeconf_awayconf_homeneutralscore_awayscore_homeq1_awayq2_awayq3_awayq4_awayot_awayq1_homeq2_homeq3_homeq4_homeot_homefirst_downs_awayfirst_downs_homethird_down_comp_awaythird_down_att_awaythird_down_comp_homethird_down_att_homefourth_down_comp_awayfourth_down_att_awayfourth_down_comp_homefourth_down_att_homepass_comp_awaypass_att_awaypass_yards_awaypass_comp_homepass_att_homepass_yards_homerush_att_awayrush_yards_awayrush_att_homerush_yards_hometotal_yards_awaytotal_yards_homefum_awayfum_homeint_awayint_homepen_num_awaypen_yards_awaypen_num_homepen_yards_homepossession_awaypossession_homeattendancetvhome_nces_namehome_lathome_lonaway_nces_nameaway_lataway_longame_latgame_lonprocessed_timeweather_temperature_2mweather_humidity_2mweather_wind_speed_10mweather_wind_direction_10mweather_precipitationweather_code
1020022.02002-08-297:00 PMregularFlorida AtlanticUSFNaNNaNNaNindFalse1051NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22074.0NaNUniversity of South Florida28.061458-82.413232Florida Atlantic University26.372421-80.10297828.061458-82.4132322025-12-07 22:33:3028.8729.41840.02
1120022.02002-08-297:00 PMregularTennessee TechBowling GreenNaNNaNNaNmacFalse741NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15696.0NaNBowling Green State University-Main Campus41.375513-83.640896Tennessee Technological University36.174754-85.50403941.375513-83.6408962025-12-07 22:33:3024.55216.2690.00
1220022.02002-08-297:30 PMregularSyracuseBYUNaNNaNbig-eastmwcFalse21427.07.07.00.00.07.014.06.015.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN65612.0NaNBrigham Young University-Provo40.250851-111.649281Syracuse University43.040176-76.13697540.250851-111.6492812025-12-07 22:33:3024.63019.11780.00
1320022.02002-08-297:30 PMregularLehighBuffaloNaNNaNNaNmacFalse3726NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN21103.0NaNUniversity at Buffalo43.000942-78.789458Lehigh University40.606822-75.38023643.000942-78.7894582025-12-07 22:33:3021.9638.4830.251
1420022.02002-08-297:30 PMregularSam Houston StateCentral MichiganNaNNaNNaNmacFalse1034NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18826.0NaNCentral Michigan University43.591203-84.775250Sam Houston State University30.714738-95.54620543.591203-84.7752502025-12-07 22:33:3024.4583.71190.02
1520022.02002-08-297:30 PMregularIndiana StateWestern MichiganNaNNaNNaNmacFalse1748NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN28268.0NaNWestern Michigan University42.282194-85.613759Indiana State University39.469073-87.40784642.282194-85.6137592025-12-07 22:33:3024.5605.8860.02
1620022.02002-08-297:35 PMregularWake ForestNorthern IllinoisNaNNaNaccmacFalse414214.07.014.00.06.07.010.07.011.07.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN19653.0NaNNorthern Illinois University41.933869-88.766428Wake Forest University36.133609-80.27744641.933869-88.7664282025-12-07 22:33:3024.2558.0630.02
1720022.02002-08-2910:00 PMregularEastern KentuckyOregon StateNaNNaNNaNpac12False10497.03.00.00.00.014.021.00.014.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNOregon State University44.563950-123.274723Eastern Kentucky University37.740845-84.30079644.563950-123.2747232025-12-07 22:33:3020.7607.43310.00
1820022.02002-08-2910:00 PMregularSan Diego StateFresno StateNaNNaNmwcwacFalse1416NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN39111.0NaNCalifornia State University-Fresno36.811154-119.744569San Diego State University32.775250-117.07122836.811154-119.7445692025-12-07 22:33:3026.6462.92700.00
1920022.02002-08-308:00 PMregularOklahomaTulsa1.0NaNbig12wacFalse3703.00.014.020.00.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN40385.0NaNUniversity of Tulsa36.152324-95.945941University of Oklahoma-Norman Campus35.209407-97.44421136.152324-95.9459412025-12-07 22:33:3028.5466.71260.03